"MAI-Thinking-1: Building a Hill-Climbing Machine" Microsoft just did something almost no frontier AI lab has done before They shared how they engineered the data behind a frontier-scale model in unusual depth. From data collection and eval decontamination, to data mix scaling, this paper lays out how they managed 30T pretraining tokens plus 3.55T midtraining tokens Surprisingly, they also used no third-party distillation and no open-source training datasets The model itself is not a jaw-dropping release, but the paper might be the best open look yet at a frontier-scale data factory and hill-climbing loop.